Distributed additive encryption and quantization for privacy preserving federated deep learning

Journal Article (2021)
Author(s)

Hangyu Zhu (University of Surrey)

R. Wang (TU Delft - Cyber Security)

Yaochu Jin (University of Surrey)

Kaitai Liang (TU Delft - Cyber Security)

Jianting Ning (Singapore Management University, Fujian Normal University)

Research Group
Cyber Security
DOI related publication
https://doi.org/10.1016/j.neucom.2021.08.062
More Info
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Publication Year
2021
Language
English
Research Group
Cyber Security
Volume number
463
Pages (from-to)
309-327

Abstract

Homomorphic encryption is a very useful gradient protection technique used in privacy preserving federated learning. However, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsuited for federated learning and vulnerable to security risks. Moreover, encrypting all model parameters is computationally intensive, especially for large machine learning models such as deep neural networks. In order to mitigate these issues, we develop a practical, computationally efficient encryption based protocol for federated deep learning, where the key pairs are collaboratively generated without the help of a trusted third party. By quantization of the model parameters on the clients and an approximated aggregation on the server, the proposed method avoids encryption and decryption of the entire model. In addition, a threshold based secret sharing technique is designed so that no one can hold the global private key for decryption, while aggregated ciphertexts can be successfully decrypted by a threshold number of clients even if some clients are offline. Our experimental results confirm that the proposed method significantly reduces the communication costs and computational complexity compared to existing encrypted federated learning without compromising the performance and security.

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